# Building Lane-Level Maps from Aerial Images

arXiv: https://arxiv.org/abs/2312.13449 · HTML: https://mlsanity.com/paper/2312.13449/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 26 references that are in the map.
- First posted on arXiv 20 Dec 2023.

## Builds on (most cited first, 21 of 21)

- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018)
- [VAD: Vectorized Scene Representation for Efficient Autonomous Driving](https://mlsanity.com/paper/2303.12077/) (2023)
- [MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2208.14437/) (2022)
- [HDMapNet: An Online HD Map Construction and Evaluation Framework](https://mlsanity.com/paper/2107.06307/) (2021)
- [PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark](https://mlsanity.com/paper/2203.11089/) (2022)
- [RoadTracer: Automatic Extraction of Road Networks from Aerial Images](https://mlsanity.com/paper/1802.03680/) (2018)
- [NDC-Scene: Boost Monocular 3D Semantic Scene Completion in Normalized Device Coordinates Space](https://mlsanity.com/paper/2309.14616/) (2023)
- [DAGMapper: Learning to Map by Discovering Lane Topology](https://mlsanity.com/paper/2012.12377/) (2020)
- [ONCE-3DLanes: Building Monocular 3D Lane Detection](https://mlsanity.com/paper/2205.00301/) (2022)
- [Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNN](https://mlsanity.com/paper/2308.08333/) (2023)
- [Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes](https://mlsanity.com/paper/2203.15302/) (2022)
- [DepthSSC: Monocular 3D Semantic Scene Completion via Depth-Spatial Alignment and Voxel Adaptation](https://mlsanity.com/paper/2311.17084/) (2023)
- [RNGDet: Road Network Graph Detection by Transformer in Aerial Images](https://mlsanity.com/paper/2202.07824/) (2022)
- [iCurb: Imitation Learning-based Detection of Road Curbs using Aerial Images for Autonomous Driving](https://mlsanity.com/paper/2103.17118/) (2021)
- [From Explicit Communication to Tacit Cooperation:A Novel Paradigm for Cooperative MARL](https://mlsanity.com/paper/2304.14656/) (2023)
- [HarmonicNeRF: Geometry-Informed Synthetic View Augmentation for 3D Scene Reconstruction in Driving Scenarios](https://mlsanity.com/paper/2310.05483/) (2023)
- [QE-BEV: Query Evolution for Bird's Eye View Object Detection in Varied Contexts](https://mlsanity.com/paper/2310.05989/) (2023)
- [DAP: Domain-aware Prompt Learning for Vision-and-Language Navigation](https://mlsanity.com/paper/2311.17812/) (2023)
- [Style Miner: Find Significant and Stable Explanatory Factors in Time Series with Constrained Reinforcement Learning](https://mlsanity.com/paper/2303.11716/) (2023)
- [Prompt-based Context- and Domain-aware Pretraining for Vision and Language Navigation](https://mlsanity.com/paper/2309.03661/) (2023)

## Built on by (most cited first, 13 of 13)

- [Exploring Diverse Methods in Visual Question Answering](https://mlsanity.com/paper/2404.13565/) (2024)
- [A Comparative Study on Enhancing Prediction in Social Network Advertisement through Data Augmentation](https://mlsanity.com/paper/2404.13812/) (2024)
- [Cross-Task Multi-Branch Vision Transformer for Facial Expression and Mask Wearing Classification](https://mlsanity.com/paper/2404.14606/) (2024)
- [Utilizing Deep Learning to Optimize Software Development Processes](https://mlsanity.com/paper/2404.13630/) (2024)
- [Diffusion Posterior Proximal Sampling for Image Restoration](https://mlsanity.com/paper/2402.16907/) (2024)
- [PrevPredMap: Exploring Temporal Modeling with Previous Predictions for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2407.17378/) (2024)
- [OpenSatMap: A Fine-grained High-resolution Satellite Dataset for Large-scale Map Construction](https://mlsanity.com/paper/2410.23278/) (2024)
- [Reusing Attention for One-stage Lane Topology Understanding](https://mlsanity.com/paper/2507.17617/) (2025)
- [Joint-Motion Mutual Learning for Pose Estimation in Videos](https://mlsanity.com/paper/2408.02285/) (2024)
- [Deep Reinforcement Learning for Mobile Robot Path Planning](https://mlsanity.com/paper/2404.06974/) (2024)
- [Lane Graph Extraction from Aerial Imagery via Lane Segmentation Refinement with Diffusion Models](https://mlsanity.com/paper/2405.00620/) (2024)
- [AID4AD: Aerial Image Data for Automated Driving Perception](https://mlsanity.com/paper/2508.02140/) (2025)
- [Recovering Global Data Distribution Locally in Federated Learning](https://mlsanity.com/paper/2409.14063/) (2024)

## Abstract (arXiv)

Detecting lane lines from sensors is becoming an increasingly significant part of autonomous driving systems. However, less development has been made on high-definition lane-level mapping based on aerial images, which could automatically build and update offline maps for auto-driving systems. To this end, our work focuses on extracting fine-level detailed lane lines together with their topological structures. This task is challenging since it requires large amounts of data covering different lane types, terrain and regions. In this paper, we introduce for the first time a large-scale aerial image dataset built for lane detection, with high-quality polyline lane annotations on high-resolution images of around 80 kilometers of road. Moreover, we developed a baseline deep learning lane detection method from aerial images, called AerialLaneNet, consisting of two stages. The first stage is to produce coarse-grained results at point level, and the second stage exploits the coarse-grained results and feature to perform the vertex-matching task, producing fine-grained lanes with topology. The experiments show our approach achieves significant improvement compared with the state-of-the-art methods on our new dataset. Our code and new dataset are available at https://github.com/Jiawei-Yao0812/AerialLaneNet.